• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.163-164
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• 2015

In order to decode the bar code image binarization process is indispensable. The traditional binarization method is a global threshold binarization and local threshold binarization. Global threshold binarization method using a single threshold. In some cases there is a blur, or if the brightness is different from the bar code image. Therefore, binary pattern information is not retained. Local threshold method is binaized pattern information is maintained but processing speed is slow than global threshold binarization. The algorithm for solving this problem, there is modified binary algorithm. In this paper, we proposed hardware IP implemented by Vivado of modified binary algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.10
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• pp.1877-1884
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• 2006

Image binarization is an important preprocessing to identify objects of interest by dividing pixels into background and objects. Usually binarization methods are classified into global and local thresholding approaches. In this paper, we propose an efficient and adaptive binarization method for the character segmentation by combining both advantages of the global and the local thresholding methods. Experimental results with the korean character images present that the proposed method binarizes character image faster and better than other local binarization methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.77-78
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• 2015

Image Binarization is essential process in the digital image processing for the read out of a one-dimensional barcode. Local threshold method is suitable for binarization of a bar code. However, It has problem that processing time is slower than other binarization algorithm. Also, It's results not appropriate If the image has a noise. In this paper, we propose the modification method for solve these problems. Proposed algorithm help to improve the speed of local thresholding method using average image. Also, we proposed a high frequency filter to one-dimensional barcode for improvement quality of binary image.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.3
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• pp.608-618
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• 2007

Image binarization is an important preprocessing to identify the object of interest by dividing pixels into the background and object. We proposes efficient binarization method and a stroke reconstruction method of the low quality character image for an effective character recognition. First, the character image is binarized by using the both advantages of local and global thresholding method and then the noise elimination around the character stroke and the hole filling on the stoke by the analysis of the binarized stroke image are performed to enhance the quality of the character stroke. Proposed binarization algorithm for character image achieved an efficiency of both processing speed and performance by the adaptive threshold selection. Moreover, We could get a high qualify binary image by a stroke reconstruction of the step-by-step denoising process.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.75-77
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• 2012

In this paper, We proposed that real-time pupil detection using local binarization at each region of eyes in image. In image obtained a single low-resolution web-camera, we detect a region of face using haar-like feature and then detect each region of eyes depending upon the rate of width and height of region of face respectively. In each region of eyes, we detect the pupil after local preprocessing and binarizing. This pupil detection can be variously used for HCI(Human-Computer Interface) systems.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.146-154
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• 2014

The local binary pattern (LBP) scheme that is one of the texture classification methods normally uses the distribution of flat, edge and corner patterns. However, it cannot examine the edge direction and the pixel difference because it is a sort of binary pattern caused by thresholding. Furthermore, since it cannot consider the pixel distribution, it shows lower performance as the image size becomes larger. In order to solve this problem, we propose a sub-classification method using the edge direction distribution and eigen-matrix. The proposed sub-classification is applied to the particular texture patches which cannot be classified by LBP. First, we quantize the edge direction and compute its distribution. Second, we calculate the distribution of the largest value among eigenvalues derived from structure matrix. Simulation results show that the proposed method provides a higher classification performance of about 8 % than the existing method.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.437-440
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• 2011

본 논문은 이동식 자동영상속도측정기로 과속 단속된 영상자료 중 역광 원인으로 자동차 번호판을 인식할 수 없어 폐기되는 영상자료에 대한 번호판 인식률을 향상시키는 알고리즘을 제안하였다. 명암값 분포가 불규칙한 자동차 번호판 이미지나 영상 자체에 손상이 많은 자동차 번호판 이미지를 지역적 적응 이진화 알고리즘을 사용함으로써, 오츠 전역적 이진화 알고리즘보다 뛰어난 자동차 번호판 인식률을 얻었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.481-483
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• 2018

AR은 VR과 달리 실세계 공간의 객체에 대한 서비스를 제공하므로 서비스 개발을 방해하는 많은 요인들이 발생한다. 이를 보완하기위해 비주얼 마커, SLAM, 객체인식 등 여러 AR 기술이 존재한다. 본 논문은 AR 기술 중에서 객체인식의 정확도 향상을 위해 지역가변 이진화(Local variable binarization)와 색상의 군집화를 사용해서 이미지에서 객체를 추출하는 방법을 제안한다. 지역 가변화는 픽셀을 순차적으로 읽어 들이면서 픽셀 주위의 값의 평균을 구하고, 이 값을 해당 픽셀의 임계 값으로 사용하는 알고리즘이다. 픽셀마다 주위 색상 값에 의해 임계 값이 변화되므로 윤곽선 표현이 기존의 이진화보다 뚜렷이 나타난다. 색상의 군집화는 객체의 중요색상과 배경의 중요색상을 중심으로 유사한 색상끼리 군집화 하는 것이다. 객체 내에서 가장 많이 나온 값과 객체 외에 가장 많이 나온 값을 각 각 기준으로 색조와 채도의 값을 Euclidean 거리를 사용해 객체의 색상과 배경 색상을 분리했다.

• Journal of Korea Multimedia Society
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• v.16 no.6
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• pp.700-707
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• 2013

In this paper, we propose a non-rigid registration method of lung parenchyma in temporal chest CT scans using region binarization modeling and locally deformable model. To cope with intensity differences between CT scans, we segment the lung vessel and parenchyma in each scan and perform binarization modeling. Then, we match them without referring any intensity information. We globally align two lung surfaces. Then, locally deformable transformation model is developed for the subsequent non-rigid registration. Subtracted quantification results after non-rigid registration are visualized by pre-defined color map. Experimental results showed that proposed registration method correctly aligned lung parenchyma in the full inspiration and expiration CT images for ten patients. Our non-rigid lung registration method may be useful for the assessment of various lung diseases by providing intuitive color-coded information of quantification results about lung parenchyma.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.07a
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• pp.446-448
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• 2011

본 논문에서는 조명 보정과 지역적인 밝기 정보를 이용한 유해 영상에서의 피부색 검출 방법을 제안한다. 첫번째, 조명의 영향을 줄이기 위하여 입력 영상을 히스토그램 평활화하여 명암 값의 분포가 한쪽으로 치우치거나 균일하지 못한 영상의 명암 값 분포를 균일화 시켜 영상을 향상될 수 있도록 한다. 그 다음, 평활화 시킨 영상을 25 개의 블록으로 분할한 후, 각 블록에서의 밝기 값에 대한 통해 평균과 왜도를 구한다. 구해진 값들을 영상의 임계값으로 설정하여 이진화 시킨다. 그리고, 평활화시킨 영상의 RGB 값을 Lab 컬러 공간으로 변환한다. 변환된 컬러 공간내의 조명 성분 값인 L(Luminance)값을 추출하여 이를 역변환 한다. 역변환한 L 값은 비정규 조명을 갖는 유해 영상의 조명에 민감한 영향을 제거하기 위하여 평활화 영상에 합한다. 마지막으로, 밝기 임계값을 통해서 얻어진 이진영상내의 객체 영역과 RGB 피부색 임계값을 통한 조명 보정된 평활화 영상내의 피부색 영역의 공통된 영역을 결과값으로 추출한다.